Abstract
The study of compounds and their metabolic pathways is crucial for predicting the presence of compounds in metabolic pathways based on the molecular properties, which can be used for drug design and metabolic pathway reconstruction. To accurately reconstruct metabolic pathways, predicting which compounds belong to specific pathways is necessary. While several computational methods have been proposed for this task, they can only map compounds to metabolic pathway classes and not actual metabolic pathways. Furthermore, similarity and feature-engineering-based methods are proposed to predict actual metabolic pathways. However, problems arise when the similarity score is below 50%, and similarity score calculations can be computationally intensive and time-consuming, especially when dealing with large datasets. To address these limitations, this paper proposes a message-based neural network (DeepMAT) by integrating a Message passing neural network (MPNN) and a multi-head attention Transformer encoder to predict the actual metabolic pathways involved by compounds. The purpose of multi-head attention transformer encoder integration is to calculate the overall information of the entire graph by calculating the influence of each node’s neighbors. Experimental results show that integrating a Message-passing network into a Transformer-style architecture is more expressive and outperforms other methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ogata, H., Goto, S., Sato, K., Fujibuchi, W., Bono, H., Kanehisa, M.: KEGG: Kyoto encyclopedia of genes and genomes. Nucl. Acids Res. 27(1), 29–34 (1999). https://doi.org/10.1093/nar/27.1.29
Okuda, S., et al.: KEGG Atlas mapping for global analysis of metabolic pathways. Nucl. Acids Res. 36(Web Server issue), 423–426 (2008). https://doi.org/10.1093/nar/gkn282
Kotera, M., Tabei, Y., Yamanishi, Y., Tokimatsu, T., Goto, S.: Supervised de novo reconstruction of metabolic pathways from metabolome-scale compound sets. Bioinformatics 29(13), 135–144 (2013). https://doi.org/10.1093/bioinformatics/btt244
Nakamura, M., Hachiya, T., Saito, Y., Sato, K., Sakakibara, Y.: An efficient algorithm for de novo predictions of biochemical pathways between chemical compounds. BMC Bioinform. 13Suppl 1(Suppl 17), S8 (2012). https://doi.org/10.1186/1471-2105-13-s17-s8
Inokuma, Y., Nishiguchi, S., Ikemoto, K., Fujita, M.: Shedding light on hidden reaction pathways in radical polymerization by a porous coordination network. Chem. Commun. 47(44), 12113–12115 (2011). https://doi.org/10.1039/c1cc15053g
Shah, H.A., Liu, J., Yang, Z., Feng, J.: Review of machine learning methods for the prediction and reconstruction of metabolic pathways. Front. Mol. Biosci. 8(June), 1–11 (2021). https://doi.org/10.3389/fmolb.2021.634141
Xavier, F.G., Balu, A., Seetharaman, S., Lakshmikandhan, A., Lawrence, A.A.E.: Alternatives to in vivo experiments – a pandect. Res. J. Pharm. Technol. 12(9), 4575–4577 (2019). https://doi.org/10.5958/0974-360X.2019.00786.8
Sorguven, E., Bozkurt, S., Baldock, C.: Computer simulations can replace in-vivo experiments for implantable medical devices. Phys. Eng. Sci. Med. 44(1), 1–5 (2021). https://doi.org/10.1007/s13246-021-00978-4
Cai, Y.D., et al.: Prediction of compounds’ biological function (metabolic pathways) based on functional group composition. Mol. Divers. 12(2), 131–137 (2008). https://doi.org/10.1007/s11030-008-9085-9
Hu, L.L., Chen, C., Huang, T., Cai, Y.D., Chou, K.C.: Predicting biological functions of compounds based on chemical-chemical interactions. PLoS ONE 6(12), e29491 (2011). https://doi.org/10.1371/journal.pone.0029491
Gao, Y.F., Chen, L., Cai, Y.D., Feng, K.Y., Huang, T., Jiang, Y.: Predicting metabolic pathways of small molecules and enzymes based on interaction information of chemicals and proteins. PLoS ONE 7(9), 1–9 (2012). https://doi.org/10.1371/journal.pone.0045944
Peng, C.-R., Lu, W.-C., Niu, B., Li, M.-J., Yang, X.-Y., Wu, M.-L.: Predicting the metabolic pathways of small molecules based on their physicochemical properties. Protein Pept. Lett. 19(12), 1250–1256 (2012). https://doi.org/10.2174/092986612803521585
Baranwal, M., Magner, A., Elvati, P., Saldinger, J., Violi, A., Hero, A.O.: A deep learning architecture for metabolic pathway prediction. Bioinformatics 36(2010), 1–7 (2019). https://doi.org/10.1093/bioinformatics/btz954
Jia, Y., Zhao, R., Chen, L.: Similarity-based machine learning model for predicting the metabolic pathways of compounds. IEEE Access 8, 130687–130696 (2020). https://doi.org/10.1109/access.2020.3009439
Yang, Z., Liu, J., Shah, H.A., Feng, J.: A novel hybrid framework for metabolic pathways prediction based on the graph attention network. BMC Bioinform. 23, 1–14 (2022). https://doi.org/10.1186/s12859-022-04856-y
Baranwal, M., et al.: A deep learning architecture for metabolic pathway prediction. Bioinformatics 36(8), 2547–2553 (2020). https://doi.org/10.1093/bioinformatics/btz954
David, L., Thakkar, A., Mercado, R., Engkvist, O.: Molecular representations in AI-driven drug discovery: a review and practical guide. J. Cheminform. 12(1), 1–22 (2020). https://doi.org/10.1186/s13321-020-00460-5
Hirohara, M., Saito, Y., Koda, Y., Sato, K., Sakakibara, Y.: Convolutional neural network based on SMILES representation of compounds for detecting chemical motif. BMC Bioinform. 19(Suppl 19), 83–94 (2018). https://doi.org/10.1186/s12859-018-2523-5
Arús-Pous, J., et al.: Randomized SMILES strings improve the quality of molecular generative models. J. Cheminform. 11(1), 1–13 (2019). https://doi.org/10.1186/s13321-019-0393-0
Zhang, Y.F., et al.: SPVec: a Word2vec-inspired feature representation method for drug-target interaction prediction. Front. Chem. 7(January), 1–11 (2020). https://doi.org/10.3389/fchem.2019.00895
Lim, S., et al.: A review on compound-protein interaction prediction methods: Data, format, representation and model. Comput. Struct. Biotechnol. J. 19, 1541–1556 (2021). https://doi.org/10.1016/j.csbj.2021.03.004
Furfari(tony), F.A.: The transformer. IEEE Ind. Appl. Mag. 8(1), 8–15 (2002). https://doi.org/10.1109/2943.974352
Deng, D., Lei, Z., Hong, X., Zhang, R., Zhou, F.: Describe molecules by a heterogeneous graph neural network with transformer-like attention for supervised property predictions. ACS Omega 7(4), 3713–3721 (2022). https://doi.org/10.1021/acsomega.1c06389
Yang, K., et al.: Analyzing learned molecular representations for property prediction. J. Chem. Inf. Model. 59(8), 3370–3388 (2019). https://doi.org/10.1021/acs.jcim.9b00237
Kim, H., Lee, J., Ahn, S., Lee, J.R.: A merged molecular representation learning for molecular properties prediction with a web-based service. Sci. Rep. 11(1), 1–9 (2021). https://doi.org/10.1038/s41598-021-90259-7
Acknowledgments
We are very thankful to Wuhan University for its generous support in conducting this research.
Funding
This work was funded by the National Key R&D Program of China (2019YFA0904303), the Major Projects of Technological Innovation in Hubei Province (2019AEA170), and the Frontier Projects of Wuhan for Application Foundation (2019010701011381).
Author information
Authors and Affiliations
Contributions
JL proposed the idea, HAS collected data, planned the formulation of the dataset, implemented the experimental study, wrote the manuscript, ZY and JF discussed the outline of the manuscript.
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
Availability of Data and Materials
The datasets used and analyzed during the current study is available at GitHub https://github.com/Hayatalishah4272/DeepMAT_pathway.
Ethics Approval and Consent to Participate
Not applicable.
Consent for Publication
Not applicable Conflicts of interest None Declared.
Competing Interests
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Shah, H.A., Liu, J., Yang, Z., Feng, J. (2023). DeepMAT: Predicting Metabolic Pathways of Compounds Using a Message Passing and Attention-Based Neural Networks. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science, vol 14088. Springer, Singapore. https://doi.org/10.1007/978-981-99-4749-2_37
Download citation
DOI: https://doi.org/10.1007/978-981-99-4749-2_37
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-4748-5
Online ISBN: 978-981-99-4749-2
eBook Packages: Computer ScienceComputer Science (R0)